Modeling runoff management strategies under climate change scenarios using hydrological simulation in the Zarrineh River Basin, Iran

Surface runoff is considered a globally pled, vital natural resource, while it has long been encountering quality and quantity issues, affecting the world's population whose daily lives are entangled with such resources (Kummu et al. 2010). In recent years, runoff generation in the changing environment has become scientific hydrological communities' focal point (Gao et al. 2014). A holistic view of the driving forces of runoff changes is crucial for efficiently using water resources and managing river flows. Climate change and human activities have tremendous potential to change the spatiotemporal runoff pattern (Zhang et al. 2014). Studies attest that climate change can complicate ecosystems and alter water resource systems (Luo et al. 2016; Fang et al. 2018; Shang et al. 2019). The drastic change in precipitation patterns emanating from climate change directly impacts the spatiotemporal pattern of water resources (Abbas & Xuan 2020). Many studies have reported that runoff changes are related to climate change (Luo et al. 2016; Zhang et al. 2016; Taheri Dehkordi et al. 2022; Zaghloul et al. 2022). Luo et al. (2016) found that climatic changes during the 1980, 1990, and 2000 decades can, in parallel, change runoff patterns in the upper zone of the Heihe River Basin by approximately 56, 61, and 93%, respectively. Shang et al. (2019) argued that after 2004, climate change has been responsible for 87% of the entire runoff changes. The results of Nash & Gleick (1991) showed that climate change had been the main contributing factor to the changes in runoff pattern in the Colorado River Basin. Therefore, the runoff change issue has always interested water communities (Milly et al. 2008). Hence, researchers are prompted to conceptualize the different impacts of climate change on runoff and provide the most practical runoff management strategies. Nowadays, a fundamental solution for water resources management in the agricultural sector is to select an efficient cropping pattern. The goal is to optimally use water resources in pursuit of profit maximization (Sepaskhah & Ghahraman 2004). Changes in cropping patterns (CCP) directly affect water consumption in the agricultural sector. Based on this premise, modification of cropping patterns is frequently referred to as a key solution for water resources management in the agricultural sector (Sun et al. 2015). Zaman et al. (2016) suggested that revising the cropping pattern can compensate for negative impacts of climate change in Siminehrud catchment and, accordingly, the inflow of Lake Urmia.

Hydrological modeling is a valuable tool to contextualize, formulate, and understand the complex hydrological processes of a river basin (Koo et al. 2020). Today, hydrologic models have been widely used to predict the climatic factors and effect of runoff changes on the hydrology of an area of interest (Takata et al. 2003; Peng et al. 2015). In case of modeling runoff for future periods considering the effects of climate change, it is pivotal to adopt rainfall–runoff models in order to predict hydrologic parameters (e.g., precipitation and temperature). The main inputs to such models are often based on the General Circulation Models (GCMs) that are downscaled using dynamic or statistical methods (Alexander et al. 2013; McSweeney et al. 2015). The Soil and Water Assessment Tool (SWAT) has been widely applied for investigating the climate impacts on the hydrological processes in several river basins (Kumar et al. 2018; Jakada & Chen 2020). The SWAT can employ different agricultural management plans for concurrent modeling of hydro-climatic drivers and their reciprocal relationships (Arnold et al. 1998; Neitsch et al. 2005). The SWAT has been used for understanding watershed responses to environmental changes and provided promising insights (Marhaento et al. 2017; Santos et al. 2021). In this regard, Zhou et al. (2018) used the SWAT hydrological model and climate elasticity method to investigate the effect of climate change and artificial interventions on the Dongjiang River Basin's runoff pattern in China and found that the SWAT can provide logical and reliable results in a basin scale.

Zarrineh River Basin (ZRB) is the major subbasin of the Lake Urmia basin. Zarrineh River (ZR) is the main inflow of Lake Urmia and hence acts as a substantial surface water resource. Lake Urmia is considered the largest wetland in Iran, the largest lake in the Middle East, and the second-largest hypersaline lake in the world. Nonetheless, the water level of Lake Urmia has been shrinking rapidly over the last 25 years (i.e., an average annual decrease of 40 cm) (Jalili et al. 2016; Ahmadaali et al. 2018). The reason is most probably attributed to the reduced inflow sources of the lake. This desiccation has caused many socioeconomic and environmental issues (Boroughani et al. 2020). The Boukan dam, a significant water infrastructure draining the region's most river tributaries, was built to feed the ailing Lake Urmia. In response to the recent changes in climatic factors and human activities, the Boukan dam's storage has almost dried up, putting the region on the verge of a perilous environmental disaster (AghaKouchak et al. 2015). A study conducted by Hassanzadeh et al. (2012) attests that climate change and rampant use of surface water resources account for almost 65% of the water-level decline of Lake Urmia. Such a multilateral issue and the existing water crises in the ZRB should be acknowledged, and more adaptive mitigation plans should be provided in response to climate change and the emanated negative impacts on the ZRB runoff and Lake Urmia. Due to the decrease in Lake Urmia's intake, many reports consider revision of the current cropping patterns as a feasible revitalization measure (Ahmadaali et al. 2018).

Although several studies have addressed the climate change impact on runoff in this region, few studies have considered the role of quantifying and modeling the practical management strategies (Ahmadzadeh et al. 2016; Yazdandoost et al. 2020). Furthermore, the effect of planting crops with suitable adaptation, low water requirement, and high economic value such as pistachio, saffron, and fodder beet in the region has rarely been addressed. The present study was aimed to assess the effect of climate change on the runoff of the ZRB using the SWAT model under two management strategies: Business-As-Usual (BAU) trend and CCP scenarios. The current cultivation pattern of the region in the first scenario was considered, and the proposed cultivation pattern based on fodder beet, saffron, and pistachio in the second scenario. The main objectives of this study were as follows: (i) simulating the runoff of zrb using the swat model, (ii) assessing the precipitation and temperature changes in the future periods, and (iii) investigating the runoff changes in the future periods based on the BAU and CCP management strategies. It is necessary to mention that the Climate Change Toolkit (CCT) was applied to downscale future GCM climate projections of temperatures and precipitation under different climate scenarios.

Meteorological, hydrological, land use/cover, DEM (digital elevation model), soil type, and agricultural data were used as input into the SWAT model. The meteorological data consists of daily maximum and minimum daily temperatures and precipitation, ranged from 1990 to 2019 and obtained from four meteorological stations: Zarrineh, Saqez, Takab, and Maragheh (Iran Meteorological Organization (IMO)). Runoff data were obtained from five hydrometric stations located at the outlets of the study area's most representative sub-basins during the 1996–2017 periods, acquired from Iran Water Resources Management (IWRM) Company. The reservoir's monthly outflow was also acquired from the IWRM Company. The thematic maps used in this study include the region's land use/cover in 2017 with a spatial resolution of 30 m × 30 m (Figure 1(c)) and a DEM with a spatial resolution of 30 m × 30 m prepared by the revival committee of Lake Urmia which is generated from the Shuttle Radar Topography Mission (SRTM) data downloaded from the United States Geological Survey (USGS) website. Due to the lack of a suitable soil database for the region, the FAO Soil Map of the World archive with a spatial resolution of 1 km was considered to produce the soil type data. The SWAT model is able to combine raster layers with different resolution to produce HRUs (Cuceloglu et al. 2017). Wang et al. (2020) also used the DEM and soil data with 30 × 30 m and 1 × 1 km resolutions, respectively, to evaluate the effects of climate change and human activities on runoff changes in the Guishui river basin, China. Agricultural information was obtained from the Iranian Ministry of Agriculture Jihad (IMAJ), including cropping patterns, planting and harvesting dates, irrigation management, and cultivated area of major crops in the ZRB.

Precipitation statistics and minimum and maximum daily temperatures data retrieved from three GCMs from ISI-MIP5 (Inter-Sectoral Impact Model Inter-Comparison Project) were used to predict the future climate under two emission scenarios: RCP 2.6 and RCP 8.5 (Hempel et al. 2013). The RCP 2.6 and RCP 8.5 emission scenarios describe the optimistic and pessimistic emission scenarios. Table 1 provides different GCMs (GFDL-ESM2M, HadGEM2-ES, and IPSL-CM5A) in detail. Based on literature review, these three GCMs were selected because they have been widely used in Iran (Abbaspour et al. 2019; Vaghefi et al. 2019; Behzadi et al. 2022). Using multiple models and emission scenarios for predicting the climatic conditions helps study different parameters and obviates potential inaccuracies in the simulation process (Knutti et al. 2010). The 1990–2019 data were used as the reference period in this work. The 2025–2049 data were generated and treated as the near future prediction. As for the far future prediction, the 2075–2099 period data were used.

The SWAT model was developed by USDA Agricultural Research Service (USDA-ARS). It is a popular physically based distributed hydrological model used in studies at the basin scale (Hu et al. 2020). The SWAT quantifies the impact of climate change and human interventions on hydrological processes. The SWAT can also simulate discharge responses based on climate change scenarios and designated land management practices. The SWAT is underpinned by a rigorous computational chain that supports the continuous calculation of daily data. It considers the impacts of surface conditions, climate change, and various water management practices to model different hydro-physical processes (e.g., agricultural chemical yields, water, sediment) (Dechmi et al. 2012). SWAT can also simulate ungauged watersheds and, most particularly, the impact of the changes in the input data such as land-use change, climate change, and various land management strategies and plans (Arnold et al. 1998; Neitsch et al. 2005).

Once the required climate and spatial data were prepared, the ZRB entire area was discretized into sub-basins, and the number of HRUs was determined. By superimposing the soil, land use, and slope layers, 14 sub-basins and 666 HRUs were created for the ZRB. Then, the reservoir characteristics (Table 2), irrigation losses and essential demands were introduced to the model through the water use management (.wus) and reservoir (.res) SWAT data files. The reservoir's monthly outflow data during the operation period were fed into the SWAT model. The 1990–2019 data range was set as the simulation period, out of which 1990–1994 opted for the warm-up period.

Different studies attested to the SWAT's capability in crop production simulation (Vaghefi et al. 2015). As for the ZRB, we defined different attributes of agricultural management, such as cropping pattern, harvesting dates, planting, and irrigation plans as close as possible to the current condition of the basin for each sub-basin in the SWAT model, striving to simulate the conditions of the region more accurately and obtain reliable results (Mahmudi et al. 2021). Therefore, we adopted the ‘irrigation schedule by date’ technique as it provides more flexible options. Crops water requirement and irrigation water sources (e.g., dams, surface and/or groundwater sources) are two pivotal inputs, which were derived from the NETWAT software and Comprehensive Water Management Plan (CWMP), respectively. The NETWAT software has been developed by IMAJ and IMO as a collaborative initiative to determine the net irrigation requirements for all cultivable crops in Iran. Current agricultural management and cropping pattern data in the ZRB are provided in detail in Table 3.

The p-factor and r-factor statistics were used to assess the goodness of fit, uncertainty degree, and the quality of the calibrated model. The p-factor represents the portion of observations within the 95 PPU interval with a 0–1 numerical scale. The r-factor is gained by dividing the average width of the 95 PPU interval by the standard deviation of the observed data. An r-factor close to 1 represents an optimal status (Abbaspour 2015). A P-factor larger than 0.7 and an R-factor smaller than 1.5 are the acceptable thresholds, although it also depends on the scale of the project and the adequacy of the data (Abbaspour et al. 2007). Hence, the P-factor larger than 0.5 can still be acceptable (Rouholahnejad et al. 2014; Monteiro et al. 2016). Attaining a larger P-factor can concurrently lead to a larger R-factor, which entails a balance. Once the desired balance is met, the model's uncertainty level is warranted. The SUFI-2 algorithm aims at achieving a high P-factor while keeping the R-factor as small as possible (Abbaspour et al. 2009).

Currently, crops with high water requirements are cultivated in the ZRB, one of the critical agricultural poles in Iran, which has caused an increase in the consumption and overexploitation of water resources and consequently a decrease in the water input of Lake Urmia. Therefore, the BAU and CCP management strategies were designed to evaluate the effect of crop cultivation change on the basin's runoff. To do so, the SWAT model would require the basin's agricultural management and irrigation information for implementing these management strategies as described in Section 2.4.

In the BAU management strategy, the effects of climate change on runoff were investigated based solely on the current agricultural management routine in the ZRB. In order to define and formulate current agricultural management in SWAT, first irrigated farming class in land use layer was partitioned into smaller pieces following the hydrological boundaries of sub-basins. The latter allowed us to introduce main crops with irrigation plans and assign them to each sub-basin. Once partitioning the previous irrigated farming class was successfully carried out, we defined the predominant crops and their cultivation area into the model. The predominant crops in the study area include Alfalfa, almonds, apples, barley, grapes, sugar beets, walnuts, and wheat. Furthermore, different variables were defined for the crops in each sub-basin, including water source, irrigation water depth, irrigation rounds, and planting/harvesting date, following the path: SWAT Input→ Sub-basin Data→ Management (.Mgt). In particular, we introduced three main water sources: rivers, dams, and unconfined aquifers. Since the irrigation plan in the study area was accustomed to traditional techniques and irrigation scheduling, we selected ‘schedule by date’ option for irrigation in operations tab. Finally, the SWAT model was implemented for future periods under two emission scenarios, based on which the future runoff values at the ZRB outlet (inflow to Lake Urmia) were calculated.

Hence, in CCP management strategy, a significant areal extent under low agro-economically productive crops with high water demand, such as Alfalfa and Apple, were replaced with those of higher economic benefits and lower water demand. The adopted CCP strategy was not constrained only by the predominant crops, but instead new crops such as saffron, fodder beet, and pistachio was introduced and their cultivation areas were defined through ‘HRU-Definition’ tab. These crops were selected for the following reasons: (i) Saffron has a higher economic value and less water requirement than alfalfa. It has a suitable ability to adapt to the region's condition. (ii) As a native species, fodder beet is used to provide livestock feed in the region and has a high ability to adapt to salinity and grow in water shortage conditions. (iii) During the field surveys, it was found that the planting of saffron by local farmers was successful as a pilot. (iv) Other researchers also suggested the cultivation of saffron instead of alfalfa in the basin (Naraqi et al. 2015; Emami & Koch 2018a). As such, saffron and fodder beet were substituted for a large portion of alfalfa cultivation, and a vast majority of the cultivation area devoted to apples and grapes were replaced with pistachio. Similar to main crops, irrigation variables were defined for the newly introduced crops. Based on this new dataset, the model was re-implemented for the future periods under the two emission scenarios, and future runoff values at the ZRB outlet were calculated.

Five hydrometric stations were used to calibrate and validate the SWAT model for ZRB, descriptive information of which is listed in Table 4. The LHOAT method embedded in the SWAT-CUP was used to conduct a sensitivity analysis of runoff simulation parameters and calibration, as detailed in Table 5. A total of 23 parameters with high sensitivity were assessed during sensitivity analysis based on which the model was calibrated and verified. In particular, the optimized zones of the sensitive parameters were automatically calibrated by the SUFI-2 algorithm in the SWAT-CUP software. For the validation purpose, the calibrated parameters were preserved for runoff simulation. The t-stat and p-value were used to quantify each parameter's sensitivity and relative significance.

The results of the sensitivity analysis provided in Table 5 show that based on p-value and t-stat values, the parameters of PLAPS, SOL-AWC, TLAPS, and CN2 are top-ranked most sensitive parameters. Hence, they play essential roles in calibration and validation of the SWAT model for runoff simulation in ZRB. The PLAPS parameter controls the orographic effect on precipitation once elevation bands are defined in sub-basins (Boithias et al. 2017). It turned out that some of the region's sub-basins pose large elevation differences. Therefore, the PLAPS parameter with the fitted value of 360.82 mm H₂O km⁻¹ played a key factor in adjusting precipitation within the river basin. A strong agreement between the observed and simulated runoff values in the calibration and validation periods indicates a successful sensitivity analysis and parameter tuning.

Based on the results, it is evident that the SWAT model showed satisfactory applicability for simulating runoff and agricultural management options in the ZRB due particularly to considering almost all the physical conditions of the basin and embedding the most representative inputs of the rainfall–runoff mechanism. The results derived from the SWAT model are in line with Valeh et al. (2021) and Santos et al. (2021) in terms of its capability in simulating runoff change under different climate change scenarios.

In the near future timescale, models G1 and G3 show an increase in precipitation in spring under both scenarios. Model G2 shows a slight decrease in precipitation in spring and summer under both scenarios; however, an increase in autumn and winter under RCP 2.6 and a decrease under RCP8.5 in the same months is evident. Moreover, the winter's share of annual precipitation under both RCP scenarios has decreased by less than 4% in the near future. In the far future period, models G1 and G3 show an increase in precipitation in winter and spring under RCP 2.6 and a decrease in precipitation in the same months under RCP 8.5. Model G2 under both scenarios shows a slight increase in precipitation in winter and spring. Models G1 and G2 under both scenarios show an increase in precipitation in summer, but in autumn, an increase in precipitation is discernible (Table 7).

The overall inferences indicate that the annual precipitation in the near future period in all the three models under both RCP scenarios slightly increases (less than 4%), except for model G2 under RCP8.5, where precipitation decreases by 2.86%. In a somewhat similar manner, the annual precipitation in the far future period in all the three models under both RCP scenarios slightly increases (less than 10%) with respect to the reference period, except for models G1 and G2 under RCP8.5 where precipitation decreases respectively by 21 and 20.2% (Table 8).

In the near and far future periods, the average minimum and maximum temperature during all months and in all three GCM models under both RCP scenarios will increase, except for model G1 under RCP2.6, where a decrease in temperature in some months is evident. The highest increase in the minimum temperature under both RCPs occurs in November. In the near and far future periods, in all scenarios, the minimum and maximum temperatures increase through all seasons, except for model G1 under RCP2.6, where temperature slightly decreases by less than 0.4 °C (Table 7). In the near future period, the annual maximum temperature of the basin in models G1, G2, and G3 under RCP2.6 increases by 0.6, 3, and 1 °C, respectively. Also, the increase under RCP 8.5 is 1.4, 3.6, and 1.8 °C. The annual minimum temperature of the basin in models G1, G2, and G3 under RCP 2.6 increases by 0.3, 1.8, and 0.7°C, respectively. Also, the increase under RCP 8.5 amounts to 0.7, 2.4 , and 1.3 °C. In the far future, the annual maximum temperature of the basin in models G1, G2, and G3 under RCP 2.6 increases by 0.7, 3, and 1.3 °C, respectively, while the increases under RCP 8.5 is 4, 7.7 °C, and 6.4%. The annual minimum temperature of the basin in models G2 and G3 under RCP 2.6 increases by 1.7 and 0.9 °C, respectively, while the increase under RCP 8.5 is 5.6 and 5.4°C. The annual minimum temperature in model G1 under RCP 2.6 decreases by 0.1 °C, while under RCP8.5 increases by 2.4 °C (Table 8).

In all three models under both RCP scenarios, seasonal fluctuations in precipitation were evident. The percentage of changes in precipitation in the far future period is discernibly higher than that of the near future. The annual precipitation in the near future period in all the three models under both RCP scenarios slightly increases. Fenta Mekonnen & Disse (2018) also predicted that the average annual precipitation increases from 2.1 to 43.8% in the Nile River in future timescales. In near and far future periods, increase of minimum and maximum temperature is higher in autumn compared to other seasons. In general, it can be inferred that the average annual minimum and maximum temperature of the ZRB in both the near and far future increases, but such an increase of minimum and maximum temperature in the far future period is higher than that of the near future. The prediction results concerning the increased temperature in the ZRB and Lake Urmia in the future periods are in line with Emami & Koch (2018a).

The simulation results of management strategies are presented as follows.

The results of the BAU strategy showed that through all climate scenarios, the increasing trend of temperature in the ZRB was stronger in autumn, which suggests that it directly stems from snow melting. This indicates that in the future period, the thawing of snow would be accelerated with respect to the past, and more significant flood events would be expected, which is in accordance with Qin et al. (2007). Zaghloul et al. (2022) also reported that the melting of glaciers in the upper Athabasca River basin, in northern Canada, due to gradual warming during cold months, especially in early spring (March and April), increased water flow. The BAU strategy also suggests that cultivated areas throughout the near and far future under the RCP2.6 and RCP8.5 scenarios can decrease the annual runoff of the ZRB, respectively, by 6–23 and 9–52%, if the continuation of the current policies is the case (i.e., BAU). Hence, the average annual inflow to Lake Urmia will be subsequently decreased. Climate change negatively affects the inflows to Lake Urmia, which signifies an urgent need to implement adaptive strategies. The prediction results concerning the decrease in annual runoff in the ZRB in the future periods are in line with previous studies in Lake Urmia (Kanani et al. 2019; Heydari Tasheh Kabood et al. 2020; Shirmohammadi et al. 2020). In parallel, Lian et al. (2021) suggested that climate change can decrease runoff by 65.64% in the Yanhe River Basin.

To assess the impact of the changes in the management strategy on runoff pattern, we replaced a large portion of Alfalfa cultivation with saffron and fodder beet since Alfalfa results in considerable irrigation water loss (∼27%). Furthermore, we substituted part of the land under apple and grape cultivation for pistachio (Table 10). Due to the important economic role of the main crops in the study area (i.e., apple, grape, and alfalfa) and the presence of supplementary industries such as fruit juice factories and local consumption of alfalfa, it was decided not to eliminate but rather reduce their cultivated areas. Accordingly, a modified cropping pattern was introduced to the SWAT model, and the impacts of climate change on the runoff in the ZRB were evaluated for both future periods under the CCP management strategies.

Studying the runoff changes in the near and far future periods under CCP management strategies revealed that the average runoff in November, December, January, and February under both scenarios is higher than the reference value, while the other months exhibit an opposite pattern. The results of both scenarios considering different models in June, July, August, and September in the ZRB are almost identical. Also, the maximum reduction in runoff was observed in July. The results showed a decrease in runoff in spring and summer with respect to the reference period in most scenarios for the near and far future periods. However, an increasing runoff trend is discernible in winter and autumn (Table 11). In the near future, the average annual runoff in most scenarios increases by 3.5–21%, except under RCP8.5 in model G2, where the average annual runoff decreases by 11.7%. In the far future, the annual runoff increases by 13–55%, except under RCP8.5 in models G1 and G3, where the average annual runoff decreases by 39.7 and 21.6%, respectively (Table 12b).

The results indicate that the CCP strategy can potentially increase runoff up to 30% more than the BAU strategy, which can subsequently increase the inflow into Lake Urmia. The latter mainly results from the resilience of Pistachio, Saffron, and Fodder beet to climate change. These results are in full agreement with Naraqi et al. (2015) and Emami & Koch (2018b) study in the ZRB that recommended partial replacement of low agro-economically productive crops with high water demand, such as alfalfa and apple, with those of higher economic benefits and lower water demand, such as canola, saffron, and pistachio. The CCP strategy was found promising also by Zaman et al. (2016) in Lake Urmia.

The runoff changes were evaluated under the climate change and land management scenarios using the SWAT model in the Zarineh River basin in northwest Iran. There were some limitations resulting from the uncertainty of hydrological models and future climate forecasting (Yu et al. 2020).

The obtained accuracy of the SWAT model was acceptable for predicting runoff changes in the study area according to the results of model calibration and validation. However, there were still many uncertainties resulting from the input data, structure and parameters used for simulation by the SWAT model. This model also does not consider snowmelt and its effect on the water cycle (Mehrazar et al. 2020; Yan et al. 2020; Yu et al. 2020).

Many RCPs-based GCMs have been proposed for future climate prediction. However, there is always uncertainty in future climate forecasting (Ouyang et al. 2015; Yu et al. 2020). In this study, the future climate was projected using the RCP2.6 and RCP8.5 scenarios. The downscaling of climatic data was also performed using the CCT model. In this regard, the main sources of uncertainty were related to the existence of a limited number of meteorological stations in the region and the incompleteness of climatic data in some of them. Future studies can focus on the uncertainty of simulating runoff changes by considering more potential RCP scenarios and the use of different downscaling methods.

This study was investigated to assess the effect of changing the cultivation pattern on the runoff prediction in the ZRB, northwestern Iran, based on the BAU and CCP scenarios in two periods of 2025–2049 and 2075–2099. The results revealed that the SWAT model could adequately and acceptably simulate the basin runoff under climate change scenarios in the region. The BAU scenario determined that the annual runoff of the basin would decrease in both future periods under the impacts associated with the current cultivation pattern because this pattern will significantly increase water consumption and decreased the water level of Lake Urmia. It was clarified that the annual runoff of ZRB increased based on the CCP scenario in the desired time periods increased the water input to Lake Urmia by changing the cultivation pattern of the basin and planting crops with less water requirement such as pistachio and saffron. The findings confirmed that it is possible to reduce the negative effects of climate change on runoff in ZRB, one of the main agricultural centers in Iran, by changing the existing cultivation pattern. Furthermore, the high demand for the expansion of agriculture in the basin will increase water consumption in the future. Therefore, changing the cultivation pattern is important in order to properly manage the region's resources and reduce the environmental disasters in Lake Urmia. The results of this study can be useful to make appropriate decisions for water management in the region. Future research should be conducted to determine the appropriate cultivation pattern and reduce its negative effects on the region's environment in order to adopt appropriate strategies to solve the problems related to the water crisis in the basin.

The authors would like to thank the Iran Meteorological Organization and the Ministry of Agriculture-Jahad of Iran for providing the required data and to express their appreciation to the anonymous reviewers and editors for their helpful comments and invaluable suggestions.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.